Overview

Dataset statistics

Number of variables27
Number of observations6522
Missing cells47
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory216.0 B

Variable types

Categorical14
Numeric13

Alerts

Destination has constant value ""Constant
To Area has constant value ""Constant
year has constant value ""Constant
Flight Date has a high cardinality: 304 distinct valuesHigh cardinality
Flight Code has a high cardinality: 86 distinct valuesHigh cardinality
day_convert has a high cardinality: 304 distinct valuesHigh cardinality
Days is highly overall correlated with day_nameHigh correlation
Block is highly overall correlated with Sold and 3 other fieldsHigh correlation
Sold is highly overall correlated with Block and 3 other fieldsHigh correlation
Left is highly overall correlated with Occ.(%) and 1 other fieldsHigh correlation
Occ.(%) is highly overall correlated with Left and 1 other fieldsHigh correlation
Block1 is highly overall correlated with Block and 3 other fieldsHigh correlation
Sold1 is highly overall correlated with Block and 3 other fieldsHigh correlation
Left1 is highly overall correlated with Occ.(%)1High correlation
Occ.(%)1 is highly overall correlated with Left1High correlation
Occ. is highly overall correlated with Left and 1 other fieldsHigh correlation
Netto is highly overall correlated with Origin and 1 other fieldsHigh correlation
Profit is highly overall correlated with prıceHigh correlation
prıce is highly overall correlated with ProfitHigh correlation
Origin is highly overall correlated with Netto and 2 other fieldsHigh correlation
day_name is highly overall correlated with DaysHigh correlation
flight_month is highly overall correlated with season and 1 other fieldsHigh correlation
season is highly overall correlated with flight_monthHigh correlation
Flight Code is highly overall correlated with Block and 9 other fieldsHigh correlation
Airline Company is highly overall correlated with Flight CodeHigh correlation
dpt is highly overall correlated with Origin and 1 other fieldsHigh correlation
dpt1 is highly overall correlated with Flight CodeHigh correlation
Netto Currency is highly overall correlated with flight_month and 1 other fieldsHigh correlation
Airline Company is highly imbalanced (51.6%)Imbalance
Left has 5653 (86.7%) zerosZeros
Left1 has 5672 (87.0%) zerosZeros

Reproduction

Analysis started2023-02-14 11:25:41.740069
Analysis finished2023-02-14 11:25:59.308294
Duration17.57 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Destination
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Turkey
6522 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters39132
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTurkey
2nd rowTurkey
3rd rowTurkey
4th rowTurkey
5th rowTurkey

Common Values

ValueCountFrequency (%)
Turkey 6522
100.0%

Length

2023-02-14T14:25:59.354292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:25:59.418849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
turkey 6522
100.0%

Most occurring characters

ValueCountFrequency (%)
T 6522
16.7%
u 6522
16.7%
r 6522
16.7%
k 6522
16.7%
e 6522
16.7%
y 6522
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32610
83.3%
Uppercase Letter 6522
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 6522
20.0%
r 6522
20.0%
k 6522
20.0%
e 6522
20.0%
y 6522
20.0%
Uppercase Letter
ValueCountFrequency (%)
T 6522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39132
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 6522
16.7%
u 6522
16.7%
r 6522
16.7%
k 6522
16.7%
e 6522
16.7%
y 6522
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 6522
16.7%
u 6522
16.7%
r 6522
16.7%
k 6522
16.7%
e 6522
16.7%
y 6522
16.7%

Origin
Categorical

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Moscow
3438 
S.Petersburg
1042 
Kazan
577 
Mineralnye Vodi
 
230
Chelyabinsk
 
196
Other values (13)
1039 

Length

Max length15
Median length6
Mean length7.5188592
Min length3

Characters and Unicode

Total characters49038
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSamara
2nd rowSamara
3rd rowSamara
4th rowSamara
5th rowSamara

Common Values

ValueCountFrequency (%)
Moscow 3438
52.7%
S.Petersburg 1042
 
16.0%
Kazan 577
 
8.8%
Mineralnye Vodi 230
 
3.5%
Chelyabinsk 196
 
3.0%
Samara 170
 
2.6%
Perm 170
 
2.6%
Ekaterinburg 141
 
2.2%
Kaliningrad 131
 
2.0%
Sochi 117
 
1.8%
Other values (8) 310
 
4.8%

Length

2023-02-14T14:25:59.478848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moscow 3438
50.9%
s.petersburg 1042
 
15.4%
kazan 577
 
8.5%
mineralnye 230
 
3.4%
vodi 230
 
3.4%
chelyabinsk 196
 
2.9%
samara 170
 
2.5%
perm 170
 
2.5%
ekaterinburg 141
 
2.1%
kaliningrad 131
 
1.9%
Other values (9) 427
 
6.3%

Most occurring characters

ValueCountFrequency (%)
o 7499
15.3%
s 4805
 
9.8%
M 3668
 
7.5%
c 3555
 
7.2%
w 3438
 
7.0%
r 3179
 
6.5%
e 3121
 
6.4%
a 2598
 
5.3%
n 1708
 
3.5%
b 1430
 
2.9%
Other values (29) 14037
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39859
81.3%
Uppercase Letter 7850
 
16.0%
Other Punctuation 1097
 
2.2%
Space Separator 230
 
0.5%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7499
18.8%
s 4805
12.1%
c 3555
8.9%
w 3438
8.6%
r 3179
8.0%
e 3121
7.8%
a 2598
 
6.5%
n 1708
 
4.3%
b 1430
 
3.6%
g 1372
 
3.4%
Other values (13) 7154
17.9%
Uppercase Letter
ValueCountFrequency (%)
M 3668
46.7%
S 1335
 
17.0%
P 1212
 
15.4%
K 708
 
9.0%
V 230
 
2.9%
C 196
 
2.5%
N 161
 
2.1%
E 141
 
1.8%
U 101
 
1.3%
T 70
 
0.9%
Other values (3) 28
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1097
100.0%
Space Separator
ValueCountFrequency (%)
230
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47709
97.3%
Common 1329
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7499
15.7%
s 4805
 
10.1%
M 3668
 
7.7%
c 3555
 
7.5%
w 3438
 
7.2%
r 3179
 
6.7%
e 3121
 
6.5%
a 2598
 
5.4%
n 1708
 
3.6%
b 1430
 
3.0%
Other values (26) 12708
26.6%
Common
ValueCountFrequency (%)
. 1097
82.5%
230
 
17.3%
- 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7499
15.3%
s 4805
 
9.8%
M 3668
 
7.5%
c 3555
 
7.2%
w 3438
 
7.0%
r 3179
 
6.5%
e 3121
 
6.4%
a 2598
 
5.3%
n 1708
 
3.5%
b 1430
 
2.9%
Other values (29) 14037
28.6%

To Area
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Antalya
6522 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters45654
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAntalya
2nd rowAntalya
3rd rowAntalya
4th rowAntalya
5th rowAntalya

Common Values

ValueCountFrequency (%)
Antalya 6522
100.0%

Length

2023-02-14T14:25:59.553392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:25:59.621938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
antalya 6522
100.0%

Most occurring characters

ValueCountFrequency (%)
a 13044
28.6%
A 6522
14.3%
n 6522
14.3%
t 6522
14.3%
l 6522
14.3%
y 6522
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39132
85.7%
Uppercase Letter 6522
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13044
33.3%
n 6522
16.7%
t 6522
16.7%
l 6522
16.7%
y 6522
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 6522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45654
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13044
28.6%
A 6522
14.3%
n 6522
14.3%
t 6522
14.3%
l 6522
14.3%
y 6522
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45654
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13044
28.6%
A 6522
14.3%
n 6522
14.3%
t 6522
14.3%
l 6522
14.3%
y 6522
14.3%

Flight Date
Categorical

Distinct304
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
30.09.2022
 
38
15.09.2022
 
38
22.09.2022
 
38
07.10.2022
 
38
06.10.2022
 
38
Other values (299)
6332 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters65220
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.5%

Sample

1st row07.06.2022
2nd row11.06.2022
3rd row14.06.2022
4th row18.06.2022
5th row21.06.2022

Common Values

ValueCountFrequency (%)
30.09.2022 38
 
0.6%
15.09.2022 38
 
0.6%
22.09.2022 38
 
0.6%
07.10.2022 38
 
0.6%
06.10.2022 38
 
0.6%
13.10.2022 38
 
0.6%
19.09.2022 38
 
0.6%
18.09.2022 38
 
0.6%
25.09.2022 38
 
0.6%
26.09.2022 38
 
0.6%
Other values (294) 6142
94.2%

Length

2023-02-14T14:25:59.690572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30.09.2022 38
 
0.6%
19.09.2022 38
 
0.6%
29.09.2022 38
 
0.6%
26.09.2022 38
 
0.6%
25.09.2022 38
 
0.6%
18.09.2022 38
 
0.6%
15.09.2022 38
 
0.6%
13.10.2022 38
 
0.6%
06.10.2022 38
 
0.6%
07.10.2022 38
 
0.6%
Other values (294) 6142
94.2%

Most occurring characters

ValueCountFrequency (%)
2 22495
34.5%
0 15183
23.3%
. 13044
20.0%
1 4699
 
7.2%
9 1754
 
2.7%
8 1747
 
2.7%
7 1725
 
2.6%
6 1588
 
2.4%
5 1273
 
2.0%
3 991
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52176
80.0%
Other Punctuation 13044
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22495
43.1%
0 15183
29.1%
1 4699
 
9.0%
9 1754
 
3.4%
8 1747
 
3.3%
7 1725
 
3.3%
6 1588
 
3.0%
5 1273
 
2.4%
3 991
 
1.9%
4 721
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 13044
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22495
34.5%
0 15183
23.3%
. 13044
20.0%
1 4699
 
7.2%
9 1754
 
2.7%
8 1747
 
2.7%
7 1725
 
2.6%
6 1588
 
2.4%
5 1273
 
2.0%
3 991
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22495
34.5%
0 15183
23.3%
. 13044
20.0%
1 4699
 
7.2%
9 1754
 
2.7%
8 1747
 
2.7%
7 1725
 
2.6%
6 1588
 
2.4%
5 1273
 
2.0%
3 991
 
1.5%

day_name
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Sunday
964 
Saturday
949 
Thursday
948 
Monday
925 
Friday
922 
Other values (2)
1814 

Length

Max length9
Median length8
Mean length7.1392211
Min length6

Characters and Unicode

Total characters46562
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowSaturday
3rd rowTuesday
4th rowSaturday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Sunday 964
14.8%
Saturday 949
14.6%
Thursday 948
14.5%
Monday 925
14.2%
Friday 922
14.1%
Wednesday 911
14.0%
Tuesday 903
13.8%

Length

2023-02-14T14:25:59.783791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:25:59.927895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
sunday 964
14.8%
saturday 949
14.6%
thursday 948
14.5%
monday 925
14.2%
friday 922
14.1%
wednesday 911
14.0%
tuesday 903
13.8%

Most occurring characters

ValueCountFrequency (%)
a 7471
16.0%
d 7433
16.0%
y 6522
14.0%
u 3764
8.1%
r 2819
 
6.1%
n 2800
 
6.0%
s 2762
 
5.9%
e 2725
 
5.9%
S 1913
 
4.1%
T 1851
 
4.0%
Other values (7) 6502
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40040
86.0%
Uppercase Letter 6522
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7471
18.7%
d 7433
18.6%
y 6522
16.3%
u 3764
9.4%
r 2819
 
7.0%
n 2800
 
7.0%
s 2762
 
6.9%
e 2725
 
6.8%
t 949
 
2.4%
h 948
 
2.4%
Other values (2) 1847
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 1913
29.3%
T 1851
28.4%
M 925
14.2%
F 922
14.1%
W 911
14.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46562
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7471
16.0%
d 7433
16.0%
y 6522
14.0%
u 3764
8.1%
r 2819
 
6.1%
n 2800
 
6.0%
s 2762
 
5.9%
e 2725
 
5.9%
S 1913
 
4.1%
T 1851
 
4.0%
Other values (7) 6502
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7471
16.0%
d 7433
16.0%
y 6522
14.0%
u 3764
8.1%
r 2819
 
6.1%
n 2800
 
6.0%
s 2762
 
5.9%
e 2725
 
5.9%
S 1913
 
4.1%
T 1851
 
4.0%
Other values (7) 6502
14.0%

flight_month
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
August
1105 
September
1102 
July
1089 
October
1067 
June
955 
Other values (7)
1204 

Length

Max length9
Median length7
Mean length5.8790248
Min length3

Characters and Unicode

Total characters38343
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJune
2nd rowJune
3rd rowJune
4th rowJune
5th rowJune

Common Values

ValueCountFrequency (%)
August 1105
16.9%
September 1102
16.9%
July 1089
16.7%
October 1067
16.4%
June 955
14.6%
May 637
9.8%
November 296
 
4.5%
December 134
 
2.1%
April 86
 
1.3%
January 33
 
0.5%
Other values (2) 18
 
0.3%

Length

2023-02-14T14:26:00.063100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 1105
16.9%
september 1102
16.9%
july 1089
16.7%
october 1067
16.4%
june 955
14.6%
may 637
9.8%
november 296
 
4.5%
december 134
 
2.1%
april 86
 
1.3%
january 33
 
0.5%
Other values (2) 18
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 6338
16.5%
u 4303
 
11.2%
t 3274
 
8.5%
r 2752
 
7.2%
b 2615
 
6.8%
J 2077
 
5.4%
y 1775
 
4.6%
m 1532
 
4.0%
o 1363
 
3.6%
c 1203
 
3.1%
Other values (16) 11111
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31821
83.0%
Uppercase Letter 6522
 
17.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6338
19.9%
u 4303
13.5%
t 3274
10.3%
r 2752
8.6%
b 2615
8.2%
y 1775
 
5.6%
m 1532
 
4.8%
o 1363
 
4.3%
c 1203
 
3.8%
p 1188
 
3.7%
Other values (8) 5478
17.2%
Uppercase Letter
ValueCountFrequency (%)
J 2077
31.8%
A 1191
18.3%
S 1102
16.9%
O 1067
16.4%
M 639
 
9.8%
N 296
 
4.5%
D 134
 
2.1%
F 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 38343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6338
16.5%
u 4303
 
11.2%
t 3274
 
8.5%
r 2752
 
7.2%
b 2615
 
6.8%
J 2077
 
5.4%
y 1775
 
4.6%
m 1532
 
4.0%
o 1363
 
3.6%
c 1203
 
3.1%
Other values (16) 11111
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6338
16.5%
u 4303
 
11.2%
t 3274
 
8.5%
r 2752
 
7.2%
b 2615
 
6.8%
J 2077
 
5.4%
y 1775
 
4.6%
m 1532
 
4.0%
o 1363
 
3.6%
c 1203
 
3.1%
Other values (16) 11111
29.0%

season
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Summer
3149 
Fall
2465 
Spring
725 
Winter
 
183

Length

Max length6
Median length6
Mean length5.2440969
Min length4

Characters and Unicode

Total characters34202
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowSummer
3rd rowSummer
4th rowSummer
5th rowSummer

Common Values

ValueCountFrequency (%)
Summer 3149
48.3%
Fall 2465
37.8%
Spring 725
 
11.1%
Winter 183
 
2.8%

Length

2023-02-14T14:26:00.135281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:26:00.209038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
summer 3149
48.3%
fall 2465
37.8%
spring 725
 
11.1%
winter 183
 
2.8%

Most occurring characters

ValueCountFrequency (%)
m 6298
18.4%
l 4930
14.4%
r 4057
11.9%
S 3874
11.3%
e 3332
9.7%
u 3149
9.2%
F 2465
 
7.2%
a 2465
 
7.2%
i 908
 
2.7%
n 908
 
2.7%
Other values (4) 1816
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27680
80.9%
Uppercase Letter 6522
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 6298
22.8%
l 4930
17.8%
r 4057
14.7%
e 3332
12.0%
u 3149
11.4%
a 2465
 
8.9%
i 908
 
3.3%
n 908
 
3.3%
p 725
 
2.6%
g 725
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S 3874
59.4%
F 2465
37.8%
W 183
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 34202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 6298
18.4%
l 4930
14.4%
r 4057
11.9%
S 3874
11.3%
e 3332
9.7%
u 3149
9.2%
F 2465
 
7.2%
a 2465
 
7.2%
i 908
 
2.7%
n 908
 
2.7%
Other values (4) 1816
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 6298
18.4%
l 4930
14.4%
r 4057
11.9%
S 3874
11.3%
e 3332
9.7%
u 3149
9.2%
F 2465
 
7.2%
a 2465
 
7.2%
i 908
 
2.7%
n 908
 
2.7%
Other values (4) 1816
 
5.3%

year
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2022
6522 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26088
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 6522
100.0%

Length

2023-02-14T14:26:00.337008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:26:00.452449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2022 6522
100.0%

Most occurring characters

ValueCountFrequency (%)
2 19566
75.0%
0 6522
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26088
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19566
75.0%
0 6522
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19566
75.0%
0 6522
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19566
75.0%
0 6522
 
25.0%

Flight Code
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct86
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
TK 3653
 
404
TK 3655
 
384
TK 3657
 
348
PC 1581
 
250
TK 3961
 
229
Other values (81)
4907 

Length

Max length8
Median length7
Mean length6.9862006
Min length6

Characters and Unicode

Total characters45564
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowWZ 4019
2nd rowWZ 4019
3rd rowWZ 4019
4th rowWZ 4019
5th rowWZ 4019

Common Values

ValueCountFrequency (%)
TK 3653 404
 
6.2%
TK 3655 384
 
5.9%
TK 3657 348
 
5.3%
PC 1581 250
 
3.8%
TK 3961 229
 
3.5%
TK 3967 193
 
3.0%
TK 3661 185
 
2.8%
TK 3963 185
 
2.8%
TK 3147 184
 
2.8%
TK 212 181
 
2.8%
Other values (76) 3979
61.0%

Length

2023-02-14T14:26:00.516692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tk 4628
35.5%
zf 940
 
7.2%
pc 415
 
3.2%
3653 404
 
3.1%
3655 384
 
2.9%
3657 348
 
2.7%
wz 259
 
2.0%
1581 250
 
1.9%
3961 229
 
1.8%
3967 193
 
1.5%
Other values (83) 4994
38.3%

Most occurring characters

ValueCountFrequency (%)
3 6844
15.0%
6522
14.3%
T 4628
10.2%
K 4628
10.2%
1 3952
8.7%
5 3548
7.8%
6 3044
6.7%
9 2640
 
5.8%
7 2095
 
4.6%
8 1213
 
2.7%
Other values (15) 6450
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25832
56.7%
Uppercase Letter 12942
28.4%
Space Separator 6522
 
14.3%
Other Punctuation 268
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 4628
35.8%
K 4628
35.8%
Z 1199
 
9.3%
F 940
 
7.3%
C 415
 
3.2%
P 415
 
3.2%
W 259
 
2.0%
E 147
 
1.1%
O 147
 
1.1%
S 95
 
0.7%
Other values (3) 69
 
0.5%
Decimal Number
ValueCountFrequency (%)
3 6844
26.5%
1 3952
15.3%
5 3548
13.7%
6 3044
11.8%
9 2640
 
10.2%
7 2095
 
8.1%
8 1213
 
4.7%
4 986
 
3.8%
2 901
 
3.5%
0 609
 
2.4%
Space Separator
ValueCountFrequency (%)
6522
100.0%
Other Punctuation
ValueCountFrequency (%)
. 268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32622
71.6%
Latin 12942
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 4628
35.8%
K 4628
35.8%
Z 1199
 
9.3%
F 940
 
7.3%
C 415
 
3.2%
P 415
 
3.2%
W 259
 
2.0%
E 147
 
1.1%
O 147
 
1.1%
S 95
 
0.7%
Other values (3) 69
 
0.5%
Common
ValueCountFrequency (%)
3 6844
21.0%
6522
20.0%
1 3952
12.1%
5 3548
10.9%
6 3044
9.3%
9 2640
 
8.1%
7 2095
 
6.4%
8 1213
 
3.7%
4 986
 
3.0%
2 901
 
2.8%
Other values (2) 877
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 6844
15.0%
6522
14.3%
T 4628
10.2%
K 4628
10.2%
1 3952
8.7%
5 3548
7.8%
6 3044
6.7%
9 2640
 
5.8%
7 2095
 
4.6%
8 1213
 
2.7%
Other values (15) 6450
14.2%

Days
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.033732
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:00.584532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0057645
Coefficient of variation (CV)0.49724785
Kurtosis-1.2520673
Mean4.033732
Median Absolute Deviation (MAD)2
Skewness-0.023250659
Sum26308
Variance4.0230914
MonotonicityNot monotonic
2023-02-14T14:26:00.632034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 964
14.8%
6 949
14.6%
4 948
14.5%
1 925
14.2%
5 922
14.1%
3 911
14.0%
2 903
13.8%
ValueCountFrequency (%)
1 925
14.2%
2 903
13.8%
3 911
14.0%
4 948
14.5%
5 922
14.1%
6 949
14.6%
7 964
14.8%
ValueCountFrequency (%)
7 964
14.8%
6 949
14.6%
5 922
14.1%
4 948
14.5%
3 911
14.0%
2 903
13.8%
1 925
14.2%

Airline Company
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Turkish Airlines
4628 
Azur Air
940 
Pegasus Airlines
 
415
Red Wings Airlines
 
259
Pegas Fly
 
147
Other values (3)
 
133

Length

Max length18
Median length16
Mean length14.640141
Min length8

Characters and Unicode

Total characters95483
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRed Wings Airlines
2nd rowRed Wings Airlines
3rd rowRed Wings Airlines
4th rowRed Wings Airlines
5th rowRed Wings Airlines

Common Values

ValueCountFrequency (%)
Turkish Airlines 4628
71.0%
Azur Air 940
 
14.4%
Pegasus Airlines 415
 
6.4%
Red Wings Airlines 259
 
4.0%
Pegas Fly 147
 
2.3%
Southwind 95
 
1.5%
Royal Flight 31
 
0.5%
Nord Wind 7
 
0.1%

Length

2023-02-14T14:26:00.701876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:26:00.777864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
airlines 5302
40.1%
turkish 4628
35.0%
azur 940
 
7.1%
air 940
 
7.1%
pegasus 415
 
3.1%
red 259
 
2.0%
wings 259
 
2.0%
pegas 147
 
1.1%
fly 147
 
1.1%
southwind 95
 
0.7%
Other values (4) 76
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 16564
17.3%
r 11817
12.4%
s 11166
11.7%
A 7182
7.5%
6686
7.0%
e 6123
 
6.4%
u 6078
 
6.4%
n 5663
 
5.9%
l 5511
 
5.8%
h 4754
 
5.0%
Other values (16) 13939
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75589
79.2%
Uppercase Letter 13208
 
13.8%
Space Separator 6686
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 16564
21.9%
r 11817
15.6%
s 11166
14.8%
e 6123
 
8.1%
u 6078
 
8.0%
n 5663
 
7.5%
l 5511
 
7.3%
h 4754
 
6.3%
k 4628
 
6.1%
z 940
 
1.2%
Other values (7) 2345
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 7182
54.4%
T 4628
35.0%
P 562
 
4.3%
R 290
 
2.2%
W 266
 
2.0%
F 178
 
1.3%
S 95
 
0.7%
N 7
 
0.1%
Space Separator
ValueCountFrequency (%)
6686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88797
93.0%
Common 6686
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 16564
18.7%
r 11817
13.3%
s 11166
12.6%
A 7182
8.1%
e 6123
 
6.9%
u 6078
 
6.8%
n 5663
 
6.4%
l 5511
 
6.2%
h 4754
 
5.4%
T 4628
 
5.2%
Other values (15) 9311
10.5%
Common
ValueCountFrequency (%)
6686
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 16564
17.3%
r 11817
12.4%
s 11166
11.7%
A 7182
7.5%
6686
7.0%
e 6123
 
6.4%
u 6078
 
6.4%
n 5663
 
5.9%
l 5511
 
5.8%
h 4754
 
5.0%
Other values (16) 13939
14.6%

dpt
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
night
3134 
afternoon
2954 
morning
 
298
evening
 
136

Length

Max length9
Median length7
Mean length6.9448022
Min length5

Characters and Unicode

Total characters45294
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowafternoon
2nd rowmorning
3rd rowafternoon
4th rowmorning
5th rowafternoon

Common Values

ValueCountFrequency (%)
night 3134
48.1%
afternoon 2954
45.3%
morning 298
 
4.6%
evening 136
 
2.1%

Length

2023-02-14T14:26:00.849411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:26:00.947001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
night 3134
48.1%
afternoon 2954
45.3%
morning 298
 
4.6%
evening 136
 
2.1%

Most occurring characters

ValueCountFrequency (%)
n 9910
21.9%
o 6206
13.7%
t 6088
13.4%
i 3568
 
7.9%
g 3568
 
7.9%
r 3252
 
7.2%
e 3226
 
7.1%
h 3134
 
6.9%
a 2954
 
6.5%
f 2954
 
6.5%
Other values (2) 434
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45294
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 9910
21.9%
o 6206
13.7%
t 6088
13.4%
i 3568
 
7.9%
g 3568
 
7.9%
r 3252
 
7.2%
e 3226
 
7.1%
h 3134
 
6.9%
a 2954
 
6.5%
f 2954
 
6.5%
Other values (2) 434
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45294
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 9910
21.9%
o 6206
13.7%
t 6088
13.4%
i 3568
 
7.9%
g 3568
 
7.9%
r 3252
 
7.2%
e 3226
 
7.1%
h 3134
 
6.9%
a 2954
 
6.5%
f 2954
 
6.5%
Other values (2) 434
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 9910
21.9%
o 6206
13.7%
t 6088
13.4%
i 3568
 
7.9%
g 3568
 
7.9%
r 3252
 
7.2%
e 3226
 
7.1%
h 3134
 
6.9%
a 2954
 
6.5%
f 2954
 
6.5%
Other values (2) 434
 
1.0%

Block
Real number (ℝ)

Distinct72
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.912297
Minimum2
Maximum492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:01.027791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16
Q140
median61
Q367
95-th percentile193
Maximum492
Range490
Interquartile range (IQR)27

Descriptive statistics

Standard deviation53.318152
Coefficient of variation (CV)0.73126419
Kurtosis1.6960347
Mean72.912297
Median Absolute Deviation (MAD)16
Skewness1.3551722
Sum475534
Variance2842.8253
MonotonicityNot monotonic
2023-02-14T14:26:01.116978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 528
 
8.1%
65 464
 
7.1%
30 415
 
6.4%
16 394
 
6.0%
163 386
 
5.9%
62 352
 
5.4%
193 341
 
5.2%
112 310
 
4.8%
34 274
 
4.2%
61 251
 
3.8%
Other values (62) 2807
43.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
5 247
3.8%
10 1
 
< 0.1%
14 1
 
< 0.1%
16 394
6.0%
18 1
 
< 0.1%
20 204
3.1%
25 3
 
< 0.1%
27 12
 
0.2%
30 415
6.4%
ValueCountFrequency (%)
492 1
 
< 0.1%
478 1
 
< 0.1%
330 5
0.1%
238 5
0.1%
237 1
 
< 0.1%
235 10
0.2%
226 1
 
< 0.1%
220 12
0.2%
209 1
 
< 0.1%
202 2
 
< 0.1%

Sold
Real number (ℝ)

Distinct175
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.574364
Minimum0
Maximum479
Zeros17
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:01.205486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q134
median60
Q365
95-th percentile188
Maximum479
Range479
Interquartile range (IQR)31

Descriptive statistics

Standard deviation51.718459
Coefficient of variation (CV)0.74335512
Kurtosis1.2295238
Mean69.574364
Median Absolute Deviation (MAD)20
Skewness1.2975657
Sum453764
Variance2674.799
MonotonicityNot monotonic
2023-02-14T14:26:01.294436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 455
 
7.0%
65 403
 
6.2%
16 345
 
5.3%
30 333
 
5.1%
163 328
 
5.0%
62 308
 
4.7%
193 283
 
4.3%
34 268
 
4.1%
112 265
 
4.1%
61 258
 
4.0%
Other values (165) 3276
50.2%
ValueCountFrequency (%)
0 17
 
0.3%
1 8
 
0.1%
2 5
 
0.1%
3 10
 
0.2%
4 13
 
0.2%
5 246
3.8%
6 6
 
0.1%
7 10
 
0.2%
8 8
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
479 1
< 0.1%
330 1
< 0.1%
326 1
< 0.1%
238 2
< 0.1%
236 1
< 0.1%
235 2
< 0.1%
231 1
< 0.1%
220 2
< 0.1%
219 1
< 0.1%
216 1
< 0.1%

Left
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3379331
Minimum-2
Maximum350
Zeros5653
Zeros (%)86.7%
Negative3
Negative (%)< 0.1%
Memory size51.1 KiB
2023-02-14T14:26:01.383109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median0
Q30
95-th percentile17
Maximum350
Range352
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.440788
Coefficient of variation (CV)4.9254397
Kurtosis94.832968
Mean3.3379331
Median Absolute Deviation (MAD)0
Skewness8.2827588
Sum21770
Variance270.29952
MonotonicityNot monotonic
2023-02-14T14:26:01.475831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5653
86.7%
1 145
 
2.2%
2 136
 
2.1%
3 59
 
0.9%
4 40
 
0.6%
16 16
 
0.2%
5 15
 
0.2%
10 15
 
0.2%
17 14
 
0.2%
8 13
 
0.2%
Other values (118) 416
 
6.4%
ValueCountFrequency (%)
-2 1
 
< 0.1%
-1 2
 
< 0.1%
0 5653
86.7%
1 145
 
2.2%
2 136
 
2.1%
3 59
 
0.9%
4 40
 
0.6%
5 15
 
0.2%
6 13
 
0.2%
7 12
 
0.2%
ValueCountFrequency (%)
350 1
< 0.1%
287 1
< 0.1%
238 2
< 0.1%
222 1
< 0.1%
209 1
< 0.1%
173 1
< 0.1%
172 1
< 0.1%
166 1
< 0.1%
165 1
< 0.1%
159 1
< 0.1%

Occ.(%)
Real number (ℝ)

Distinct102
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.072984
Minimum0
Maximum103
Zeros17
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:01.560482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68
Q1100
median100
Q3100
95-th percentile100
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.068678
Coefficient of variation (CV)0.15684616
Kurtosis19.501395
Mean96.072984
Median Absolute Deviation (MAD)0
Skewness-4.4166854
Sum626588
Variance227.06506
MonotonicityNot monotonic
2023-02-14T14:26:01.642670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5654
86.7%
98 103
 
1.6%
97 82
 
1.3%
99 74
 
1.1%
96 25
 
0.4%
94 24
 
0.4%
95 23
 
0.4%
93 23
 
0.4%
90 20
 
0.3%
88 17
 
0.3%
Other values (92) 477
 
7.3%
ValueCountFrequency (%)
0 17
0.3%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 6
 
0.1%
9 4
 
0.1%
10 1
 
< 0.1%
11 5
 
0.1%
ValueCountFrequency (%)
103 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 5654
86.7%
99 74
 
1.1%
98 103
 
1.6%
97 82
 
1.3%
96 25
 
0.4%
95 23
 
0.4%
94 24
 
0.4%

dpt1
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
evening
3394 
morning
2875 
afternoon
 
235
night
 
18

Length

Max length9
Median length7
Mean length7.066544
Min length5

Characters and Unicode

Total characters46088
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmorning
2nd rownight
3rd rowmorning
4th rownight
5th rowmorning

Common Values

ValueCountFrequency (%)
evening 3394
52.0%
morning 2875
44.1%
afternoon 235
 
3.6%
night 18
 
0.3%

Length

2023-02-14T14:26:01.758379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:26:01.887511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
evening 3394
52.0%
morning 2875
44.1%
afternoon 235
 
3.6%
night 18
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 13026
28.3%
e 7023
15.2%
i 6287
13.6%
g 6287
13.6%
v 3394
 
7.4%
o 3345
 
7.3%
r 3110
 
6.7%
m 2875
 
6.2%
t 253
 
0.5%
a 235
 
0.5%
Other values (2) 253
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46088
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 13026
28.3%
e 7023
15.2%
i 6287
13.6%
g 6287
13.6%
v 3394
 
7.4%
o 3345
 
7.3%
r 3110
 
6.7%
m 2875
 
6.2%
t 253
 
0.5%
a 235
 
0.5%
Other values (2) 253
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 46088
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 13026
28.3%
e 7023
15.2%
i 6287
13.6%
g 6287
13.6%
v 3394
 
7.4%
o 3345
 
7.3%
r 3110
 
6.7%
m 2875
 
6.2%
t 253
 
0.5%
a 235
 
0.5%
Other values (2) 253
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 13026
28.3%
e 7023
15.2%
i 6287
13.6%
g 6287
13.6%
v 3394
 
7.4%
o 3345
 
7.3%
r 3110
 
6.7%
m 2875
 
6.2%
t 253
 
0.5%
a 235
 
0.5%
Other values (2) 253
 
0.5%

Block1
Real number (ℝ)

Distinct86
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.781202
Minimum0
Maximum492
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:01.989152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q140
median61
Q367
95-th percentile193
Maximum492
Range492
Interquartile range (IQR)27

Descriptive statistics

Standard deviation53.493413
Coefficient of variation (CV)0.73498942
Kurtosis1.7367526
Mean72.781202
Median Absolute Deviation (MAD)16
Skewness1.3484437
Sum474679
Variance2861.5453
MonotonicityNot monotonic
2023-02-14T14:26:02.234941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 529
 
8.1%
65 461
 
7.1%
30 405
 
6.2%
163 386
 
5.9%
62 350
 
5.4%
193 342
 
5.2%
112 310
 
4.8%
16 298
 
4.6%
34 262
 
4.0%
61 252
 
3.9%
Other values (76) 2927
44.9%
ValueCountFrequency (%)
0 7
 
0.1%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 7
 
0.1%
4 5
 
0.1%
5 234
3.6%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 6
 
0.1%
11 5
 
0.1%
ValueCountFrequency (%)
492 2
 
< 0.1%
330 5
 
0.1%
238 6
 
0.1%
235 11
 
0.2%
221 1
 
< 0.1%
220 11
 
0.2%
209 1
 
< 0.1%
202 2
 
< 0.1%
195 1
 
< 0.1%
193 342
5.2%

Sold1
Real number (ℝ)

Distinct178
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.648114
Minimum0
Maximum494
Zeros53
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:02.311495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q134
median60
Q365
95-th percentile193
Maximum494
Range494
Interquartile range (IQR)31

Descriptive statistics

Standard deviation53.093641
Coefficient of variation (CV)0.76231269
Kurtosis1.8039698
Mean69.648114
Median Absolute Deviation (MAD)20
Skewness1.3577279
Sum454245
Variance2818.9347
MonotonicityNot monotonic
2023-02-14T14:26:02.432285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 501
 
7.7%
65 432
 
6.6%
30 364
 
5.6%
163 324
 
5.0%
193 306
 
4.7%
62 299
 
4.6%
112 257
 
3.9%
34 247
 
3.8%
61 246
 
3.8%
54 236
 
3.6%
Other values (168) 3310
50.8%
ValueCountFrequency (%)
0 53
 
0.8%
1 14
 
0.2%
2 9
 
0.1%
3 23
 
0.4%
4 33
 
0.5%
5 210
3.2%
6 16
 
0.2%
7 10
 
0.2%
8 10
 
0.2%
9 9
 
0.1%
ValueCountFrequency (%)
494 1
 
< 0.1%
472 1
 
< 0.1%
333 1
 
< 0.1%
330 3
< 0.1%
238 3
< 0.1%
237 1
 
< 0.1%
236 1
 
< 0.1%
235 6
0.1%
221 2
 
< 0.1%
220 6
0.1%

Left1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.133088
Minimum-6
Maximum214
Zeros5672
Zeros (%)87.0%
Negative41
Negative (%)0.6%
Memory size51.1 KiB
2023-02-14T14:26:02.519671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile0
Q10
median0
Q30
95-th percentile16.95
Maximum214
Range220
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.348792
Coefficient of variation (CV)4.8989342
Kurtosis59.873843
Mean3.133088
Median Absolute Deviation (MAD)0
Skewness7.1749204
Sum20434
Variance235.58541
MonotonicityNot monotonic
2023-02-14T14:26:02.595781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5672
87.0%
1 158
 
2.4%
2 83
 
1.3%
3 38
 
0.6%
4 30
 
0.5%
5 25
 
0.4%
-1 21
 
0.3%
8 20
 
0.3%
7 20
 
0.3%
6 18
 
0.3%
Other values (116) 437
 
6.7%
ValueCountFrequency (%)
-6 1
 
< 0.1%
-4 1
 
< 0.1%
-3 6
 
0.1%
-2 12
 
0.2%
-1 21
 
0.3%
0 5672
87.0%
1 158
 
2.4%
2 83
 
1.3%
3 38
 
0.6%
4 30
 
0.5%
ValueCountFrequency (%)
214 1
 
< 0.1%
182 2
< 0.1%
171 2
< 0.1%
162 1
 
< 0.1%
160 2
< 0.1%
159 2
< 0.1%
157 2
< 0.1%
156 4
0.1%
152 1
 
< 0.1%
151 1
 
< 0.1%

Occ.(%)1
Real number (ℝ)

Distinct102
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.537718
Minimum0
Maximum103
Zeros53
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:02.691727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64.05
Q1100
median100
Q3100
95-th percentile100
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.879991
Coefficient of variation (CV)0.17668405
Kurtosis18.383384
Mean95.537718
Median Absolute Deviation (MAD)0
Skewness-4.3175362
Sum623097
Variance284.93409
MonotonicityNot monotonic
2023-02-14T14:26:02.785715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5669
86.9%
98 74
 
1.1%
0 53
 
0.8%
99 49
 
0.8%
97 40
 
0.6%
94 33
 
0.5%
101 31
 
0.5%
96 28
 
0.4%
80 25
 
0.4%
88 19
 
0.3%
Other values (92) 501
 
7.7%
ValueCountFrequency (%)
0 53
0.8%
1 3
 
< 0.1%
2 5
 
0.1%
3 5
 
0.1%
4 4
 
0.1%
5 5
 
0.1%
6 7
 
0.1%
7 4
 
0.1%
9 6
 
0.1%
10 10
 
0.2%
ValueCountFrequency (%)
103 1
 
< 0.1%
102 6
 
0.1%
101 31
 
0.5%
100 5669
86.9%
99 49
 
0.8%
98 74
 
1.1%
97 40
 
0.6%
96 28
 
0.4%
95 17
 
0.3%
94 33
 
0.5%

Occ.
Real number (ℝ)

Distinct416
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.073789
Minimum0
Maximum102.94
Zeros17
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:02.879693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67.6955
Q1100
median100
Q3100
95-th percentile100
Maximum102.94
Range102.94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.074811
Coefficient of variation (CV)0.15690868
Kurtosis19.495636
Mean96.073789
Median Absolute Deviation (MAD)0
Skewness-4.4162035
Sum626593.25
Variance227.24993
MonotonicityNot monotonic
2023-02-14T14:26:02.987058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5653
86.7%
96.67 30
 
0.5%
0 17
 
0.3%
98.39 16
 
0.2%
90 14
 
0.2%
98.44 13
 
0.2%
98.21 13
 
0.2%
87.5 11
 
0.2%
93.33 11
 
0.2%
98.57 10
 
0.2%
Other values (406) 734
 
11.3%
ValueCountFrequency (%)
0 17
0.3%
1.72 1
 
< 0.1%
1.79 1
 
< 0.1%
2.27 1
 
< 0.1%
3.33 2
 
< 0.1%
3.45 1
 
< 0.1%
3.57 2
 
< 0.1%
4.69 1
 
< 0.1%
5 1
 
< 0.1%
5.17 1
 
< 0.1%
ValueCountFrequency (%)
102.94 1
 
< 0.1%
101.63 1
 
< 0.1%
100.54 1
 
< 0.1%
100 5653
86.7%
99.55 1
 
< 0.1%
99.48 1
 
< 0.1%
99.47 8
 
0.1%
99.46 7
 
0.1%
99.39 7
 
0.1%
99.29 6
 
0.1%

Netto
Real number (ℝ)

Distinct618
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.17692
Minimum150
Maximum750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.1 KiB
2023-02-14T14:26:03.075720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile321.12
Q1353.37
median376.52
Q3447.4225
95-th percentile700
Maximum750
Range600
Interquartile range (IQR)94.0525

Descriptive statistics

Standard deviation105.67562
Coefficient of variation (CV)0.25391995
Kurtosis2.2600345
Mean416.17692
Median Absolute Deviation (MAD)30.13
Skewness1.6207566
Sum2714305.9
Variance11167.337
MonotonicityNot monotonic
2023-02-14T14:26:03.153502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
370 437
 
6.7%
353.24 308
 
4.7%
369.24 308
 
4.7%
377.18 293
 
4.5%
375 270
 
4.1%
356.36 252
 
3.9%
353.37 240
 
3.7%
400 216
 
3.3%
703.18 168
 
2.6%
398 142
 
2.2%
Other values (608) 3888
59.6%
ValueCountFrequency (%)
150 3
< 0.1%
161 1
 
< 0.1%
167 1
 
< 0.1%
170 1
 
< 0.1%
171 1
 
< 0.1%
172 1
 
< 0.1%
173 1
 
< 0.1%
174 1
 
< 0.1%
175 2
 
< 0.1%
176 6
0.1%
ValueCountFrequency (%)
750 80
1.2%
703.18 168
2.6%
700 126
1.9%
693.38 22
 
0.3%
660.36 126
1.9%
657.37 120
1.8%
641 1
 
< 0.1%
625 1
 
< 0.1%
623.52 3
 
< 0.1%
605 1
 
< 0.1%

Netto Currency
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
EUR
3838 
USD
2684 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19566
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowEUR
3rd rowEUR
4th rowEUR
5th rowEUR

Common Values

ValueCountFrequency (%)
EUR 3838
58.8%
USD 2684
41.2%

Length

2023-02-14T14:26:03.230357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:26:03.299837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
eur 3838
58.8%
usd 2684
41.2%

Most occurring characters

ValueCountFrequency (%)
U 6522
33.3%
E 3838
19.6%
R 3838
19.6%
S 2684
13.7%
D 2684
13.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 19566
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 6522
33.3%
E 3838
19.6%
R 3838
19.6%
S 2684
13.7%
D 2684
13.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 19566
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 6522
33.3%
E 3838
19.6%
R 3838
19.6%
S 2684
13.7%
D 2684
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 6522
33.3%
E 3838
19.6%
R 3838
19.6%
S 2684
13.7%
D 2684
13.7%

Profit
Real number (ℝ)

Distinct6132
Distinct (%)94.7%
Missing47
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean329.89562
Minimum-555.91
Maximum2552.46
Zeros0
Zeros (%)0.0%
Negative251
Negative (%)3.8%
Memory size51.1 KiB
2023-02-14T14:26:03.366419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-555.91
5-th percentile24.479
Q1199.935
median328.97
Q3448.68
95-th percentile623.751
Maximum2552.46
Range3108.37
Interquartile range (IQR)248.745

Descriptive statistics

Standard deviation194.91639
Coefficient of variation (CV)0.59084261
Kurtosis5.6228664
Mean329.89562
Median Absolute Deviation (MAD)123.76
Skewness0.82434892
Sum2136074.1
Variance37992.397
MonotonicityNot monotonic
2023-02-14T14:26:03.460976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347.61 3
 
< 0.1%
222.86 3
 
< 0.1%
149.1 3
 
< 0.1%
407.7 3
 
< 0.1%
207.09 3
 
< 0.1%
341.48 3
 
< 0.1%
372.69 3
 
< 0.1%
383.44 3
 
< 0.1%
480.39 3
 
< 0.1%
468.14 3
 
< 0.1%
Other values (6122) 6445
98.8%
(Missing) 47
 
0.7%
ValueCountFrequency (%)
-555.91 1
< 0.1%
-277.85 1
< 0.1%
-277.21 1
< 0.1%
-262.51 1
< 0.1%
-255.65 1
< 0.1%
-235.81 1
< 0.1%
-224.27 1
< 0.1%
-223.38 1
< 0.1%
-219.79 1
< 0.1%
-200.76 1
< 0.1%
ValueCountFrequency (%)
2552.46 1
< 0.1%
1921.86 1
< 0.1%
1660.82 1
< 0.1%
1635.96 1
< 0.1%
1629.08 1
< 0.1%
1578.38 1
< 0.1%
1457.82 1
< 0.1%
1444.63 1
< 0.1%
1426.03 1
< 0.1%
1375.4 1
< 0.1%

prıce
Real number (ℝ)

Distinct6344
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.69518
Minimum-99.21
Maximum3255.64
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)0.1%
Memory size51.1 KiB
2023-02-14T14:26:03.545303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-99.21
5-th percentile397.593
Q1598.54
median732.125
Q3858.015
95-th percentile1145.9615
Maximum3255.64
Range3354.85
Interquartile range (IQR)259.475

Descriptive statistics

Standard deviation236.54982
Coefficient of variation (CV)0.31807362
Kurtosis6.0579897
Mean743.69518
Median Absolute Deviation (MAD)129.8
Skewness1.2376797
Sum4850380
Variance55955.817
MonotonicityNot monotonic
2023-02-14T14:26:03.632942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.12 6
 
0.1%
396.04 4
 
0.1%
749.1 4
 
0.1%
693.38 4
 
0.1%
827.22 3
 
< 0.1%
321.12 3
 
< 0.1%
606.97 3
 
< 0.1%
150 3
 
< 0.1%
703.41 2
 
< 0.1%
470 2
 
< 0.1%
Other values (6334) 6488
99.5%
ValueCountFrequency (%)
-99.21 1
< 0.1%
-84.51 1
< 0.1%
-76.65 1
< 0.1%
-41.79 1
< 0.1%
-20.76 1
< 0.1%
79.11 1
< 0.1%
79.25 1
< 0.1%
94.22 1
< 0.1%
94.98 1
< 0.1%
95.06 1
< 0.1%
ValueCountFrequency (%)
3255.64 1
< 0.1%
2625.04 1
< 0.1%
2339.14 1
< 0.1%
2332.26 1
< 0.1%
2281.56 1
< 0.1%
2161 1
< 0.1%
2147.81 1
< 0.1%
2129.21 1
< 0.1%
2050.97 1
< 0.1%
2035.76 1
< 0.1%

day_convert
Categorical

Distinct304
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2022-09-30
 
38
2022-09-15
 
38
2022-09-22
 
38
2022-10-07
 
38
2022-10-06
 
38
Other values (299)
6332 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters65220
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.5%

Sample

1st row2022-06-07
2nd row2022-06-11
3rd row2022-06-14
4th row2022-06-18
5th row2022-06-21

Common Values

ValueCountFrequency (%)
2022-09-30 38
 
0.6%
2022-09-15 38
 
0.6%
2022-09-22 38
 
0.6%
2022-10-07 38
 
0.6%
2022-10-06 38
 
0.6%
2022-10-13 38
 
0.6%
2022-09-19 38
 
0.6%
2022-09-18 38
 
0.6%
2022-09-25 38
 
0.6%
2022-09-26 38
 
0.6%
Other values (294) 6142
94.2%

Length

2023-02-14T14:26:04.199022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-09-30 38
 
0.6%
2022-09-19 38
 
0.6%
2022-09-29 38
 
0.6%
2022-09-26 38
 
0.6%
2022-09-25 38
 
0.6%
2022-09-18 38
 
0.6%
2022-09-15 38
 
0.6%
2022-10-13 38
 
0.6%
2022-10-06 38
 
0.6%
2022-10-07 38
 
0.6%
Other values (294) 6142
94.2%

Most occurring characters

ValueCountFrequency (%)
2 22495
34.5%
0 15183
23.3%
- 13044
20.0%
1 4699
 
7.2%
9 1754
 
2.7%
8 1747
 
2.7%
7 1725
 
2.6%
6 1588
 
2.4%
5 1273
 
2.0%
3 991
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52176
80.0%
Dash Punctuation 13044
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22495
43.1%
0 15183
29.1%
1 4699
 
9.0%
9 1754
 
3.4%
8 1747
 
3.3%
7 1725
 
3.3%
6 1588
 
3.0%
5 1273
 
2.4%
3 991
 
1.9%
4 721
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 13044
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22495
34.5%
0 15183
23.3%
- 13044
20.0%
1 4699
 
7.2%
9 1754
 
2.7%
8 1747
 
2.7%
7 1725
 
2.6%
6 1588
 
2.4%
5 1273
 
2.0%
3 991
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22495
34.5%
0 15183
23.3%
- 13044
20.0%
1 4699
 
7.2%
9 1754
 
2.7%
8 1747
 
2.7%
7 1725
 
2.6%
6 1588
 
2.4%
5 1273
 
2.0%
3 991
 
1.5%

Interactions

2023-02-14T14:25:57.635633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:43.195661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.946102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.924320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.878990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.883472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.003361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.402742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.493259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.382615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.859411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.961923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.233916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.752205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:43.275249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.041209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.990074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.946513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.016504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.122415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.462641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.561070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.528291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.935979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.100228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.394872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.820748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:43.346870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.113459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.055064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.029041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.087408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.250452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.545583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.622421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.616444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.010056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.252082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.461879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.927328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:43.418042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.191992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.118911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.114585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.166521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.422939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.635967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.684309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.695601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.087601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.357567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.524422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.074880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:43.519901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.288287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.188743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.200598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.243159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.538645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.714888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.765111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.777314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.157957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.427104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.602990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.211967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.311393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.361606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.284559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.265377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.303703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.619885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.882391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.828684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.852057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.235695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.494667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.667989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.280968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.420259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.423815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.352504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.328758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.416654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.724765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.978829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.893524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.918265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.311136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.628499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.814099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.346504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.485101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.499741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.412346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.395223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.511458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.805426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.050605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.961690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.380657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.385546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.735038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.885119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.417121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.560733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.559259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.472875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.453685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.569578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:49.872170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.125147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.026951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.453206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.447102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.811574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.951650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.484123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.638526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.639875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.557884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.524280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.667501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.003337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.203865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.107897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.531198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.514163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.881590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.127715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.610345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.704426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.715655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.640456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.614406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.755098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.089108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.282353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.161914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.632548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.658992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:55.948119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.278295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.693849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.774113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.785484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.746628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.698497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.857374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.197844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.350622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.241826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.719756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.759723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.025652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.357822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:58.761848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:44.864821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:45.854474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:46.809556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:47.806770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:48.929045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:50.310578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:51.424316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:52.308771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:53.788053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:54.832293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:56.091192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:25:57.525017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-14T14:26:04.271754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
DaysBlockSoldLeftOcc.(%)Block1Sold1Left1Occ.(%)1Occ.NettoProfitprıceOriginday_nameflight_monthseasonFlight CodeAirline Companydptdpt1Netto Currency
Days1.0000.0140.023-0.0340.0350.0110.017-0.0450.0460.0350.0410.0350.0450.1251.0000.0390.0090.2450.0560.0770.0830.018
Block0.0141.0000.9480.097-0.0820.9920.939-0.0290.046-0.083-0.262-0.078-0.2180.3260.0200.2960.2090.7520.4420.2480.1970.343
Sold0.0230.9481.000-0.1150.1290.9410.886-0.0050.0230.129-0.269-0.006-0.1370.3010.0090.1500.1080.7130.3610.1920.1330.332
Left-0.0340.097-0.1151.000-0.9990.0940.101-0.0520.050-0.9990.029-0.288-0.2760.0610.0080.2220.1600.2990.1410.0670.0740.071
Occ.(%)0.035-0.0820.129-0.9991.000-0.080-0.0870.050-0.0500.999-0.0330.2860.2720.0780.0160.2300.1860.1870.0620.0580.0650.102
Block10.0110.9920.9410.094-0.0801.0000.945-0.0220.040-0.080-0.262-0.078-0.2180.3150.0200.2940.2080.7520.4330.2430.2000.352
Sold10.0170.9390.8860.101-0.0870.9451.000-0.2320.248-0.088-0.254-0.008-0.1490.2980.0210.2970.2210.7080.4100.2360.1840.360
Left1-0.045-0.029-0.005-0.0520.050-0.022-0.2321.000-0.9910.0520.038-0.251-0.2050.0340.0000.1660.2360.1780.0610.0680.0580.232
Occ.(%)10.0460.0460.0230.050-0.0500.0400.248-0.9911.000-0.050-0.0410.2530.2030.0130.0170.1930.3050.0920.0230.0520.0300.281
Occ.0.035-0.0830.129-0.9990.999-0.080-0.0880.052-0.0501.000-0.0330.2870.2720.0780.0130.2300.1860.1880.0620.0570.0640.101
Netto0.041-0.262-0.2690.029-0.033-0.262-0.2540.038-0.041-0.0331.0000.0060.3620.5160.0620.3370.3050.7330.4750.3770.3000.476
Profit0.035-0.078-0.006-0.2880.286-0.078-0.008-0.2510.2530.2870.0061.0000.8910.1040.0260.3000.3340.2360.1380.0930.0960.429
prıce0.045-0.218-0.137-0.2760.272-0.218-0.149-0.2050.2030.2720.3620.8911.0000.1660.0260.3550.3900.3460.2940.1450.1640.271
Origin0.1250.3260.3010.0610.0780.3150.2980.0340.0130.0780.5160.1040.1661.0000.1250.0750.1000.9950.4390.5880.4190.385
day_name1.0000.0200.0090.0080.0160.0200.0210.0000.0170.0130.0620.0260.0260.1251.0000.0390.0090.2450.0560.0770.0830.018
flight_month0.0390.2960.1500.2220.2300.2940.2970.1660.1930.2300.3370.3000.3550.0750.0391.0000.9990.4080.3540.1930.2090.641
season0.0090.2090.1080.1600.1860.2080.2210.2360.3050.1860.3050.3340.3900.1000.0090.9991.0000.4580.2810.0980.0970.440
Flight Code0.2450.7520.7130.2990.1870.7520.7080.1780.0920.1880.7330.2360.3460.9950.2450.4080.4581.0000.9940.9150.9100.697
Airline Company0.0560.4420.3610.1410.0620.4330.4100.0610.0230.0620.4750.1380.2940.4390.0560.3540.2810.9941.0000.3590.3500.458
dpt0.0770.2480.1920.0670.0580.2430.2360.0680.0520.0570.3770.0930.1450.5880.0770.1930.0980.9150.3591.0000.4610.157
dpt10.0830.1970.1330.0740.0650.2000.1840.0580.0300.0640.3000.0960.1640.4190.0830.2090.0970.9100.3500.4611.0000.050
Netto Currency0.0180.3430.3320.0710.1020.3520.3600.2320.2810.1010.4760.4290.2710.3850.0180.6410.4400.6970.4580.1570.0501.000

Missing values

2023-02-14T14:25:58.880406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-14T14:25:59.207741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DestinationOriginTo AreaFlight Dateday_nameflight_monthseasonyearFlight CodeDaysAirline CompanydptBlockSoldLeftOcc.(%)dpt1Block1Sold1Left1Occ.(%)1Occ.NettoNetto CurrencyProfitprıceday_convert
0TurkeySamaraAntalya07.06.2022TuesdayJuneSummer2022WZ 40192Red Wings Airlinesafternoon50500100morning501492100.00518.00EUR220.49738.492022-06-07
1TurkeySamaraAntalya11.06.2022SaturdayJuneSummer2022WZ 40196Red Wings Airlinesmorning50500100night50321864100.00534.00EUR283.83817.832022-06-11
2TurkeySamaraAntalya14.06.2022TuesdayJuneSummer2022WZ 40192Red Wings Airlinesafternoon50500100morning5044688100.00546.00EUR278.68824.682022-06-14
3TurkeySamaraAntalya18.06.2022SaturdayJuneSummer2022WZ 40196Red Wings Airlinesmorning50500100night50500100100.00595.00EUR211.23806.232022-06-18
4TurkeySamaraAntalya21.06.2022TuesdayJuneSummer2022WZ 40192Red Wings Airlinesafternoon50500100morning5048296100.00596.00EUR278.98874.982022-06-21
5TurkeySamaraAntalya22.06.2022WednesdayJuneSummer2022ZF 63913Azur Airnight30300100evening3032710100.00463.73EUR147.08610.812022-06-22
6TurkeySamaraAntalya24.06.2022FridayJuneSummer2022ZF 63915Azur Airnight30300100evening300300100.00463.73EUR188.16651.892022-06-24
7TurkeySamaraAntalya25.06.2022SaturdayJuneSummer2022ZF 63916Azur Airnight30300100evening30191163100.00463.73EUR163.93627.662022-06-25
8TurkeySamaraAntalya25.06.2022SaturdayJuneSummer2022WZ 40196Red Wings Airlinesafternoon50500100morning5043786100.00605.00EUR239.87844.872022-06-25
9TurkeySamaraAntalya27.06.2022MondayJuneSummer2022ZF 63911Azur Airnight3029197evening3011193796.67463.73EUR319.13782.862022-06-27
DestinationOriginTo AreaFlight Dateday_nameflight_monthseasonyearFlight CodeDaysAirline CompanydptBlockSoldLeftOcc.(%)dpt1Block1Sold1Left1Occ.(%)1Occ.NettoNetto CurrencyProfitprıceday_convert
6512TurkeyS.PetersburgAntalya15.07.2022FridayJulySummer2022TK 36735Turkish Airlinesnight65650100morning65650100100.0377.18EUR419.47796.652022-07-15
6513TurkeyS.PetersburgAntalya15.07.2022FridayJulySummer2022TK 37165Turkish Airlinesafternoon64640100evening64640100100.0377.18EUR553.40930.582022-07-15
6514TurkeyS.PetersburgAntalya15.07.2022FridayJulySummer2022TK 39615Turkish Airlinesafternoon46460100morning46460100100.0343.18EUR487.99831.172022-07-15
6515TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 36576Turkish Airlinesafternoon54540100evening54540100100.0377.18EUR480.39857.572022-07-16
6516TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 36576Turkish Airlinesafternoon550100evening550100100.0703.18EUR697.931401.112022-07-16
6517TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 36736Turkish Airlinesnight65650100morning65650100100.0377.18EUR420.95798.132022-07-16
6518TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 39616Turkish Airlinesafternoon46460100morning46460100100.0343.18EUR398.44741.622022-07-16
6519TurkeyS.PetersburgAntalya17.07.2022SundayJulySummer2022TK 12347Turkish Airlinesafternoon34340100morning34340100100.0370.00USD512.82882.822022-07-17
6520TurkeyS.PetersburgAntalya17.07.2022SundayJulySummer2022TK 36577Turkish Airlinesafternoon54540100evening54540100100.0377.18EUR467.51844.692022-07-17
6521TurkeyS.PetersburgAntalya17.07.2022SundayJulySummer2022TK 36577Turkish Airlinesafternoon550100evening550100100.0703.18EUR361.371064.552022-07-17